A reduction type method for nonlinear semi-infinite programming

Publications

A new algorithm to identify all global maximizers based on simulated annealing

Publication . Pereira, Ana I.; Fernandes, Edite M.G.P.

In this work we consider the problem of finding all the global maximizers of a given nonlinear optimization problem. We propose a new algorithm that combines the simulated annealing (SA) method with a function stretching technique, to generate a sequence of global maximization problems that are defined whenever a new maximizer is identified. To find the global maximizers, we apply the SA algorithm to the sequence of maximization problems. Results of numerical experiments with a set of well-known test problems show that the proposed method is effective. We also compare the performance of our algorithm with other multi-global optimizers.

2005Conference object

Open access

A reduction method for semi-infinite programming by means of a global stochastic approach

Publication . Pereira, Ana I.; Fernandes, Edite M.G.P.

We describe a reduction algorithm for solving semi-infinite programming problems. The proposed algorithm uses the simulated annealing method equipped with a function stretching as a multi-local procedure, and a penalty technique for the finite optimization process. An exponential penalty merit function is reduced along each search direction to ensure convergence from any starting point. Our preliminary numerical results seem to show that the algorithm is very promising in practice.

2009Journal article

Open access

Numerical experiments with a continuous L-2-exponential merit function for semi-infinite programming

Publication . Pereira, Ana I.; Fernandes, Edite M.G.P.

Here, we present some numerical experiments with a reduction method for solving nonlinear semi-infinite programming (SIP) problems. The method relies on a line search technique to ensure a sufficient decrease of a L_2-exponential merit function. The proposed merit function is continuous for SIP and improves the algorithm efficiency when compared with other previously tested merit functions. A comparison with other reduction methods is also included.

2008Conference object

Open access